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Filter an ATAC peak universe with explicit criteria

Source: R/FilterATACPeaks.R
FilterATACPeaks.Rd

Select peaks by raw-count support, interval width, chromosome, and optional blacklist overlap, then create a clean counts-only assay.

Usage

FilterATACPeaks(
  object,
  assay = NULL,
  new.assay = "filtered_peaks",
  min.cells = 0L,
  min.counts = 0,
  min.width = 1L,
  max.width = Inf,
  seqlevels = NULL,
  blacklist = NULL,
  verbose = TRUE
)

Arguments

object

A Seurat object with a validated ChromatinAssay.

assay

Source chromatin assay; defaults to the active assay.

new.assay

An unused assay name, default "filtered_peaks".

min.cells

Minimum number of cells with a nonzero raw count, inclusive.

min.counts

Minimum sum of raw counts across cells, inclusive.

min.width, max.width

Inclusive integer interval-width limits in bases. The default retains every positive width; max.width may be Inf.

seqlevels

Optional unique chromosome names to retain. Names must occur in the source reference; chromosome styles are never guessed.

blacklist

Optional GRanges of excluded 1-based closed intervals. Any overlap excludes a peak regardless of strand. The blacklist must declare the same genome label as the source assay. Known chromosome lengths must agree and interval ends must not exceed them. Chromosomes absent from the source assay are retained as supplied; names are never converted.

verbose

Whether to print progress messages.

Value

The Seurat object with the filtered ChromatinAssay. All cells and retained peak order/counts are preserved. The new assay retains reference, gene annotation and fragment references, but not normalized/scaled data, variable features, motifs, links or downstream results. Assay initialization puts raw counts in the data layer. Source assays, active assay and existing reductions are unchanged. Misc(object[[new.assay]], "scop_peak_filter") stores one row per source peak with each criterion and the final keep flag. At least two peaks and two cells must remain for assay construction.

See also

CheckATACData(), AnnotateATACPeaks()

Examples

counts <- Matrix::sparseMatrix(
  i = c(1, 2, 1), j = c(1, 2, 2), x = c(2, 3, 1), dims = c(2, 2),
  dimnames = list(c("p1", "p2"), c("cell1", "cell2"))
)
ranges <- GenomicRanges::GRanges(c("chr1:1-50", "chr1:101-150"))
object <- ReadATACData(counts, ranges = ranges, genome = "hg38")
filtered <- FilterATACPeaks(object, min.cells = 1, verbose = FALSE)
rownames(filtered[["filtered_peaks"]])
#> [1] "chr1-1-50"    "chr1-101-150"